Southern Iran Research Articles

Influences of Geological and Petrophysical Attributes on Electrical Resistivity–Based Reserve Evaluation: Enhancing Carbonate Reservoir Classification, Permian-Triassic Reservoirs of Southern Iran

Summary The strong heterogeneity of carbonate reservoirs makes it challenging to assess the spatial distribution of fluid behavior, geological attributes, petrophysical properties, and estimate oil reserves. The diversity in facies and diagenetic processes contributes significantly to the heterogeneity in these reservoirs. The ability to accurately characterize and manage hydrocarbon reservoirs heterogeneity hinges on a comprehensive understanding of geological and petrophysical attributes such as water saturation, porosity, permeability, and electrical conductivity. In the quest for optimizing hydrocarbon reservoir identification and management, understanding the intricate relationship between the geological, petrophysical characteristics, and electrical conductivity of reservoir rocks is paramount. Electrical conductivity serves as an indicator of the structural attributes of pore networks, reflecting sedimentary and diagenetic influences on reservoir quality. Despite the extensive use of electrical resistivity in hydrocarbon reservoir assessments, accurate interpretation of its variations remains a significant challenge, which is due to complex geological factors. Thus, acquiring a thorough insight into the impact of geological and petrophysical attributes on electrical resistivity is crucial for a dependable assessment of hydrocarbon reservoirs. In this article, we explore the complex interplay between various geological and petrophysical factors and their impact on the electrical conductivity of rocks, which serves as a vital parameter in the assessment of hydrocarbon reservoirs. The focus is on how sediment texture, pore types, depositional environments, diagenetic processes, and characteristics of pore throat radius influence the electrical properties of rocks. The objective of this comprehensive approach is to decipher the geological and petrophysical modifications using electrical data, thereby improving the analysis of electrical discrepancies. Moreover, the accuracy of categorizing rocks based on their electrical characteristics is evaluated to effectively manage reservoir heterogeneity. Different data were gathered from an exploratory well situated in the western Persian Gulf. This data set comprised evaluations of 1,370 thin sections, 1,110 porosity and permeability data, 32 scanning electron microscopy (SEM) analyses, wireline logs, 29 mercury injection capillary pressure (MICP) data, 58 formation resistivity factors (FRFs), 20 formation resistivity indexes (FRIs), and 157 Dean-Stark measurements. Rocks were classified into different groups with similar electrical behavior utilizing the electrical quality index (EQI) approach. The efficiency of this method in managing reservoir heterogeneity, as one of the techniques for determining electrical rock type, was assessed, particularly by comparing the precision of predicted water saturation with Dean-Stark saturation data. The findings of this study demonstrated that diagenetic processes, especially dolomitization and dissolution, have the most significant impact on variations in the electrical conductivity of rocks. These processes govern pore size, distribution, pore types, and the radii of pore throats. According to the findings of this research, categorizing rocks based on their electrical characteristics improves the precision of water saturation predictions. In additionally, the use of constant Archie coefficients in water saturation calculations results in an overestimation of water saturation, leading to an underestimation of hydrocarbon reserves in the reservoir. The results of this research allow for more knowledgeable decision-making concerning reservoir efficiency, hydrocarbon reserves, production techniques, and increased oil recovery.

Epidemiological and clinical characteristics of long COVID-19 among Iranians: A community-based study in southern Iran

BackgroundThe study aimed to evaluate the prevalence and pattern of long COVID-19 (LC) symptoms among individuals who had contracted COVID-19, to calculate the incidence of LC, and to provide insights into risk factors associated with developing LC in this population.MethodsThis population-based cross-sectional survey was conducted in Fars province in 2023. Adult participants with a history of COVID-19 were recruited using a cluster random sampling method, alongside a control group with similar characteristics through the same methodology. Data were collected through in-person interviews using two researcher-developed data collection forms focused on demographic and clinical information.ResultsA total of 2010 participants, comprising 1561 (77.7%) and 449 (22.3%) individuals with and without a previous history of COVID-19 were included. Among those with COVID-19 history, the prevalence of experiencing any symptoms was 93.7% (95% CI of 92.3%-94.8%) during the disease acute phase and 36.4% (95% CI of 34.0%-38.8%) after recovery. The incidence of symptoms specifically related to COVID-19, calculated by comparing the symptom rates between participants with and without a history of COVID-19, was found to be 13%. Factors such as older age, previous hospitalization for COVID-19, presence of cardiovascular disease, and use of steroids/chemotherapy were associated with LC symptoms.ConclusionsOur investigation sheds light on long-term aspects of COVID-19, demonstrating a significant prevalence of LC with diverse manifestations. It also underscores the importance of establishing standardized criteria and control groups in research on LC to address challenges related to heterogeneity and potential overestimation of symptoms.

Assessing the Prevalence of Lead Toxicity and Related Factors in Pediatric Patients with Abdominal Pain

Background: Lead toxicity is an environmental disease that has been encountered since ancient times and can present with symptoms such as abdominal pain, constipation, or neurological deficits. This issue accounts for the highest burden among environmental disorders. Objectives: The aim of this study was to assess the prevalence of lead toxicity in patients with abdominal pain in Southern Iran. Methods: In this study, 50 pediatric patients with chronic abdominal pain and 50 healthy individuals were compared in terms of their blood lead levels and underlying demographic information that could contribute to lead toxicity. Results: A total of 100 participants were enrolled in this study, with a mean age of 8.50 ± 2.444 years, 53.0% of whom were male. The mean blood lead levels in the control and case groups were 1.54 ± 0.693 and 2.73 ± 2.547 µg/dL, respectively, with a statistically significant difference (P = 0.002). When cutoff values of 2 µg/dL and 3.5 µg/dL were considered for lead toxicity, the prevalence of lead toxicity was significantly higher in the case group. Environmental and demographic factors did not differ between patients with lead toxicity and those with normal lead levels (P > 0.05). Conclusions: The prevalence of lead toxicity in children complaining of chronic abdominal pain was 12.0% (95% CI: 4.5% - 24.3%). However, more accurate testing of the environment is needed in future studies.

Examination of ethical intelligence and cognitive flexibility of nurses and their role in predicting the level of patient privacy protection

IntroductionPossessing ethical intelligence and cognitive flexibility can play a significant role in the acceptable performance of nurses. Furthermore, respecting the privacy of patients should always be a primary ethical principle that nurses focus on. This study aimed to investigate the ethical intelligence and cognitive flexibility of nurses and their role in predicting the level of patients’ privacy observance. Also, determining the overall status of patient privacy protection and its two domains, namely human dignity domain and maintaining personal privacy, were specific objectives of this study.MethodsThis cross-sectional descriptive-analytical study was conducted in 2022. A sample of 340 nurses and 1067 patients from teaching hospitals affiliated with Shiraz University of Medical Sciences in southern Iran were selected. Standard questionnaires of ethical intelligence and cognitive flexibility were used for nurses, and a privacy observance questionnaire was used for patients. Data were analyzed using t-tests, ANOVA, Pearson correlation coefficient, and multiple linear regression with the SPSS23 software.ResultsThe mean score of ethical intelligence and cognitive flexibility for nurses was 98.33 ± 18.06 (out of 200) and 74.56 ± 16.76 (out of 140), respectively. The mean score of patients’ privacy observance was 79.74 ± 14.53 (out of 150). The results of multiple linear regression showed that the dimensions of perseverance and assertiveness towards rights (β = 0.540, p < 0.001), action based on principles, values, and beliefs (β = 0.454, p < 0.001), responsibility towards personal decisions (β = 0.410, p < 0.001), accepting responsibility for serving others (β = 0.393, p < 0.001), ability to forgive one’s mistakes (β = 0.301, p = 0.001), ability to forgive others’ mistakes (β = 0.287, p = 0.002), honesty (β = 0.275, p = 0.004), acknowledgment of mistakes and failures (β = 0.263, p = 0.005), commitment to promises (β = 0.242, p = 0.005), and interest in others (β = 0.237, p = 0.01) from the dimensions of ethical intelligence, as well as the dimensions of perceived control (β = 0.580, p < 0.001), perception of multiple solutions (β = 0.511, p < 0.001), and perception of justifications (β = 0.373, p < 0.001) from the dimensions of cognitive flexibility had a positive and significant effect on the level of patients’ privacy observance.ConclusionEthical intelligence and cognitive flexibility of nurses and the level of patient privacy protection were estimated to be at a moderate level. Also, the level of ethical intelligence and cognitive flexibility of nurses played a predictive role in the level of patients’ privacy observance. It is suggested that hospital managers and policymakers enhance nurses’ ethical intelligence and cognitive flexibility through educational, welfare, managerial, motivational, and job-related programs, thereby improving the status of patient privacy protection.

The economic burden of bipolar disorder: a case study in Southern Iran

BackgroundBipolar Disorder (BD) imposes considerable economic and social burdens on the community. Therefore, the present study aimed to determine the economic burden of bipolar disorder in patients referred to single-specialty psychiatric hospitals at the secondary and tertiary care level in 2022.MethodsThis partial economic evaluation was conducted as a cross-sectional study in the south of Iran in 2022, and 916 patients were selected through the census method. The prevalence-based and bottom-up approaches were used to collect cost information and calculate the costs, respectively. The data on Direct Medical Costs (DMC), Direct Non-Medical Costs (DNMC), and Indirect costs (IC) were obtained using the information from the patients’ medical records and bills as well as the self-reports by the patients or their companions. The human capital approach was also used to calculate IC.Findings: The results showed that in 2022, the annual cost of bipolar disorder was $4,227 per patient. The largest share of the costs was that of DMC (77.66%), with hoteling and ordinary beds accounting for the highest expenses (55.40%). The shares of DNMC and IC were 6.37% and 15.97%, respectively, and the economic burden of the disease in the country was estimated at $2,799,787,266 as well.ConclusionIn general, the costs of bipolar disorder treatment could impose a heavy economic burden on the community, the health system, the insurance system, and the patients themselves. Considering the high costs of hoteling and ordinary beds, it is suggested that hospitalization of BD patients be reduced by managing treatment solutions along with prevention methods to reduce the economic burden of this disease. Furthermore, in order to reduce the costs, proper and fair distribution of psychiatrists and psychiatric beds as well as expansion of home care services and use of the Internet and virtual technologies to follow up the treatment of these patients are recommended.

Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques

Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios.In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directionsbetween 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predictedby the RFR model using SAR interferometry along two perpendicular profiles confirmedthe model's precision.

Economic burden of psoriasis in Southern Iran in 2022

IntroductionPsoriasis is a common, chronic, and non-contagious skin disease that has no known cause or treatment. Various medical costs for skin disorders, including psoriasis, can be expensive and lifelong. The purpose of the present study was to determine the economic burden of psoriasis in patients admitted to general hospitals affiliated with Shiraz University of Medical Sciences, Iran in 2022.Materials and methodsThis research was a descriptive, cross-sectional, cost of illness study from a societal perspective. All psoriasis patients (N = 118) admitted to the hospitals affiliated with Shiraz University of Medical Sciences in 2022 were examined. 7 participants refused to cooperate and were excluded from the study and, the information of 111 patients was collected. A researcher-made data collection form was used to collect data. A prevalence-based approach was used to prepare cost data, and the costing approach was bottom-up. The productivity lost due to the absenteeism of patients and their companions was estimated using the human capital approach. Microsoft Excel ® 2016 was applied to analyze the data.ResultsThe mean annual cost per psoriasis patient was estimated to be US$ 30,374.21. Its highest and lowest share was related to direct medical costs (88.61%), direct non-medical costs (7.3%) and indirect costs (4.09%), respectively. Also, the highest mean direct medical, direct non-medical, and indirect costs per patient were related to those of medicine (93.11%), transportation (51.65%), and absenteeism of the patients’ companions due to patient care (71.73%).ConclusionConsidering that the major contributor in the direct medical cost of treating psoriasis patients was related to medicine, designing appropriate mechanisms for insurance coverage, and allocating government subsidies for the purchase of medicine, are suggested. The result of the current study has important implications for policymakers in developing guidelines for early diagnosis of this disease and reducing the health economic burden.

Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data

ObjectivesCerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. MethodsThe Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. ResultsA total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC = 0.910, Recall = 0.73, Precision = 0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50 % missing rate were included (AUROC = 0.887, Recall = 0.77, Precision = 0.86). The random forest model yielded the best precision by using variables with less than 50 % missing rate (AUROC = 0.882, Recall = 0.61, Precision = 0.94). ConclusionThe application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

Challenges, barriers and solutions for implementing clinical practice guidelines: a qualitative study in southern Iran

BackgroundIdentifying the challenges of implementing clinical practice guidelines (CPGs) can provide valuable information for decision-makers and health policymakers at the national and local levels. The implementation of CPGs requires the...

Emerging Risk to Dengue in Asian Metropolitan Areas Under Global Warming

AbstractAedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre‐industrial temperatures, using multi‐model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi‐model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year.

A Study of Antimicrobial Resistance Rates in Clinical Burn Center Isolates of Pseudomonas aeruginosa in the South of Iran

Background: This research aimed to examine the frequency of resistance to antimicrobial agents and their variations in Pseudomonas aeruginosa clinical isolates, which are accountable for invasive infections in the southern part of Iran, from 2018 to 2022. Methods: A retrospective study was conducted, involving the gathering of microbiological data from Taleghani Burn Hospital from 2018 to 2022. The primary variables under scrutiny were department, sample origin, the antiprogram system (e.g., disc diffusion and strip methods, and clinical laboratory standards), and the rate or percentage of resistant isolates investigated by antimicrobial susceptibility testing. The interpretation criteria employed for the study were those of the Clinical Laboratory Standards Institute (CLSI). And the percentage of resistant isolates was also taken into consideration. Results: The disc diffusion and strip method is the most commonly used approach for antimicrobial susceptibility testing. According to the CLSI, resistance rates ranged from 3.64% (colistin) to 77.38% (amikacin). The rates of antimicrobial resistance remained relatively constant over time in 2018-2022. Approximately 67.62% of isolates were multidrug resistant, and the remaining 9.46% were extensively drug-resistant. Wound and urine isolates demonstrated higher resistance, except for amikacin and piperacillin, than those from blood culture and biopsy. Conclusion: Antimicrobial resistance is widely prevalent in P. aeruginosa, a common bacterium in southern Iran. The findings revealed the highest resistance rates for commonly used antibiotics such as amikacin, piperacillin, ceftazidime, and meropenem. However, colistin and nitrofurantoin are more effective against this bacterium. The wound and urine isolates represented the highest resistance rates, indicating the need for prompt and appropriate treatment. Interestingly, the resistance rates for most antibiotics remained relatively stable during the study period, emphasizing the need to develop alternative treatments for P. aeruginosa infections.

Assessing The Relationship Between Quality Of Life, Health-Promoting Lifestyle And Body Mass Index In Medical Students Of Southern Iran: A Cross-Sectional Study

Background — Obesity is a condition caused by the interaction of complex factors, which include genetics and behavioral components, such as physical activity and diet. Obesity has a negative impact on physical performance, quality of life (QoL), and health-promoting lifestyle (HPL). The purpose of our study was to investigate the relationship of QoL and HPL with body mass index (BMI) in medical students of Southern Iran. Methods — This cross-sectional study was conducted in 2021 on 536 students in Southern Iran. Data collection was completed using standardized QoL and HPL questionnaires and anthropometric measurements. Data were analyzed by chi-squared test and one-way ANOVA, assuming significance level of p&lt;0.05, using the IBM SPSS Statistics 21 software package. Results — The mean age of study participants was 21.33±2.03 years. Over 88% of them were female; 347 (64.7%) students had normal BMI and 189 (35.3%) had abnormal BMI. Among the dimensions of QoL were physical health (p=0.03), mental health (p&lt;0.001) and general health (p=0.01). We revealed statistically significant differences in students with different BMI levels. Among the dimensions of HPL, stress management (p&lt;0.001), physical activity (p&lt;0.001) and nutrition (p&lt;0.05) exhibited statistically significant differences in groups based on BMI. Conclusion — Based on the results of this study, we concluded that it is necessary to plan multiple interventions, especially in the form of continuous and short-term training courses, and to encourage medical students to pursue a healthy lifestyle, especially in terms of nutrition, physical activity and stress management.

Decoupled Hydroclimate of Central and Southwestern Iran Controlled by the Strength of Southerly‐Westerly Jets During Marine Isotope Stage 3

AbstractThe regional impact of abrupt glacial climate variability remains poorly constrained for arid southwestern Asia, particularly winter dynamics during Marine Isotope Stage 3, due to limited paleoarchives in the Middle East. Here, we present continuous speleothem records of δ18O and δ13C with robust chronologies for southwestern and central Iran, spanning ∼50–30 ka. Stable‐isotope signals in the two stalagmites are generally uncorrelated and do not exhibit a consistent response to Greenland stadials or interstadials; however, both show a positive δ18O excursion that coincides with Heinrich event 4. We explore the potential mechanisms for intermittent coupling of speleothem δ18O across Iran through isotope‐enabled atmospheric modeling outputs, from which we utilize the spatial δ18O gradient as a proxy for wintertime westerly versus southerly jet strength. Our results suggest that during Heinrich event 4 and several Greenland stadials, stronger westerly winds enhanced Mediterranean moisture contributions to both sites and reduced aridity in southern Iran.

Uncovering a Seismogenic Fault in Southern Iran through Co-Seismic Deformation of the Mw 6.1 Doublet Earthquake of 14 November 2021

On 14 November 2021, a doublet earthquake, each event of which had an Mw of 6.1, struck near Fin in the Simply Folded Belt (SFB) in southern Iran. The first quake occurred at 12:07:04 UTC, followed by a second one just a minute and a half later. The SFB is known for its blind thrust faults, typically not associated with surface ruptures. These earthquakes are usually linked to the middle and lower layers of the sedimentary cover. Identifying the faults that trigger earthquakes in the region remains a significant challenge and is subject to high uncertainty. This study aims to identify and determine the fault(s) that may have caused the doublet earthquake. To achieve this goal, we utilized the DInSAR method using Sentinel-1 to detect deformation, followed by finite-fault inversion and magnetic interpretation to determine the location, geometry, and slip distribution of the fault(s). Bayesian probabilistic joint inversion was used to model the earthquake sources and derive the geometric parameters of potential fault planes. The study presents two potential fault solutions—one dipping to the north and the other to the south. Both solutions showed no significant difference in strike and fault location, suggesting a single fault. Based on the results of the seismic inversion, it appears that a north-dipping fault with a strike, dip, and rake of 257°, 74°, and 77°, respectively, is more consistent with the geological setting of the area. The fault plane has a width of roughly 3.6 km, a length of 13.4 km, and a depth of 5.6 km. Our results revealed maximum displacements along the radar line of sight reaching values of up to −360 mm in the ascending orbit, indicating an unknown fault with horizontal displacements at the surface ranging from −144 to 170 mm and maximum vertical displacements between −204 and 415 mm. Aeromagnetic data for Iran were utilized with an average flight-line spacing of 7.5 km. The middle of the data observation period was considered to apply the RTP filter, and the DRTP method was used. We calculated the gradient of the residual anomaly in the N-S direction due to the direction of the existing faults and folds. The gradient map identified the fault and potential extension of the observed anomalies related to a fault with an ENE-WSW strike, which could extend to the ~ E-W. We suggest that earthquakes occur in the sedimentary cover of the SFB where subsurface faulting is involved, with Hormuz salt acting as an important barrier to rupture. The multidisciplinary approach used in this study, including InSAR and magnetic data, underscores the importance of accurate fault characterization. These findings provide valuable insights into the seismic hazard of the area.

Effectiveness of polypill for primary and secondary prevention of cardiovascular disease: a pragmatic cluster-randomised controlled trial (PolyPars)

BackgroundWe aimed to investigate the effectiveness of fixed-dose combination therapy (polypill) for primary and secondary prevention of major cardiovascular diseases in a typical rural setting.MethodsThe PolyPars Study is a two-arm...

FaultQuake: An open-source Python tool for estimating Seismic Activity Rates in faults

In regions experiencing ongoing aseismic deformation, fault’s Activity Rate (AR) calculations often lead to an overestimation of hazard potential. This study proposes a novel methodology that integrates the Seismic Coupling Coefficient (SCC) into the fault Seismic Activity Rate (SAR) calculation process to discriminate seismic moment rates. We introduce FaultQuake, an open-source Python tool equipped with a Graphical User Interface (GUI), designed to implement this methodology and accurately estimate SAR for faults. These activity rates can be included in Probabilistic Seismic Hazard Assessment (PSHA) frameworks and assist in differentiating the seismic and aseismic deformation. FaultQuake also presents an innovative embedded workflow, the Optimal Value Computation Workflow (OVCW), based on Conflation of Probabilities (CoP), for calculating the Maximum Magnitude (Mmax) from the empirical relationships and the observed magnitudes (Mobs) assigned to a single fault. This enhancement improves the estimation of seismic moment rates and the SAR calculation process. FaultQuake outputs are provided in the format of OpenQuake engine input files to facilitate the PSHA process. We present a sample case study focusing on the PSHA of a region in southern Iran characterized by a substantial aseismic deformation to illustrate the practical application of FaultQuake in seismic hazard analysis. Peak Ground Acceleration (PGA) maps for 10% and 2% Probabilities of Exceedance (PoE) are plotted to compare PGAs with and without applying the FaultQuake algorithm. The results provide an enhanced view of the area’s hazard with mitigation of the overestimation, resulting in more representative hazard maps. The source codes of FaultQuake are available at the FaultQuake GitHub repository, contributing to the computer and geoscience community.

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies.

Ultra-mini Percutaneous Nephrolithotomy (UM-PCNL) with a Semi-rigid Ureteroscope in Preschool Children: An Innovative Experience in Southern Iran.

Percutaneous nephrolithotomy (PCNL) has undergone extensive modification to reduce complications. One of the recent advances in minimally invasive procedures is the advent of ultra-mini PCNL (UM-PCNL), which provides miniaturized access to the kidney. However, the specific instruments applied in UM-PCNL may not be found in centers with limited resources. This study evaluated the safety, feasibility, results, and short-term complication rates of performing UM-PCNL using a semi-rigid ureteroscope in preschool children. Between September 2013 and September 2021, a total of 68 patients, including 42 boys and 26 girls with a mean age of 3.2 ± 2.4 years, underwent UM-PCNL with a 4.5French tip ureteroscope instead of an ultra-mini nephroscope in children aged less than 7 years old. The procedure was done under general anesthesia in the prone position. The nephrostomy tract was dilated to 12F. Stones were fragmented using a pneumatic lithotripter. Irrigation was done with normal saline. The early stone-free rate (SFR) was 91%, and the short-term total SFR was 97%. No statistically significant difference was found in pre-operative and post-operative Hb, BUN, Cr, Na+, and K+. Fever (11 patients) and ileus (5 patients) constituted the majority of complications, and only one patient required a blood transfusion. None of the cases undergoing UM-PCNL with this method required a re-do PCNL. Our experience shows that with sufficient experience in handling semi-rigid ureteroscopes, urologists practicing in centers with limited resources could perform UM-PCNL with relatively favorable outcomes.

A Comprehensive Review of the Situation of Visceral Leishmaniasis Vectors in Iran.

This study's major aim is to investigate the situation of visceral leishmaniasis vectors, with a focus on their distribution and relationships to the disease in Iran and some other old-world nations. The terms Iran and the Old World, along with the keywords sand flies, vectors, visceral leishmaniasis, distribution, and Phlebotomus, were searched in electronic databases from 1930 to 2018, including Pub-Med, Web of Science, Google Scholar, and MEDLINE. According to the findings, Phlebotomus tobbi was a mountain species, but it was also found in the plains, rodent nests, and rock crevices. This species was considered to be one of the vectors of visceral leishmaniasis in Iran. Phlebotomus kandelakii has been caught in Afghanistan, Iran, Lebanon, Turkey and Georgia. In Iran, the first infection of Phlebotomus kandelakii and Ph. perfiliewi transcaucasicus with Leishmania infantum were reported in northwest of Iran. Phlebotomus major was one of the complex sand flies, and its members include Ph. major (India, Nepal, and Pakistan), Ph. neglectus (Southern Europe, Crimea and Iran), Ph. syriacus (Southwest Asia, Caucasus), Ph. notus (Afghanistan and Iran), Ph. wenyoni (Iran and Iraq), and Ph. wui (China). The first natural infection of Ph. alexandri with Leishmania infantum was reported in 2006 in endemic foci of visceral leishmaniasis in Fars Province, southern Iran. The findings of this project suggest that sand fly species of the subgenus Larrousisus play a key role in the transmission of all types of visceral leishmaniasis in Iran.

The cytoskeletal protein profilin is an important allergen in saltwort (Salsola kali).

Pollen from Salsola kali, i.e., saltwort, Russian thistle, is a major allergen source in the coastal regions of southern Europe, in Turkey, Central Asia, and Iran. S. kali-allergic patients mainly suffer from hay-fever (i.e., rhinitis and conjunctivitis), asthma, and allergic skin symptoms. The aim of this study was to investigate the importance of individual S. kali allergen molecules. Sal k 1, Sal k 2, Sal k 3, Sal k 4, Sal k 5, and Sal k 6 were expressed in Escherichia coli as recombinant proteins containing a C-terminal hexahistidine tag and purified by nickel affinity chromatography. The purity of the recombinant allergens was analyzed by SDS-PAGE. Their molecular weight was determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and their fold and secondary structure were studied by circular dichroism (CD) spectroscopy. Sera from clinically well-characterized S. kali-allergic patients were used for IgE reactivity and basophil activation experiments. S. kali allergen-specific IgE levels and IgE levels specific for the highly IgE cross-reactive profilin and the calcium-binding allergen from timothy grass pollen, Phl p 12 and Phl p 7, respectively, were measured by ImmunoCAP. The allergenic activity of natural S. kali pollen allergens was studied in basophil activation experiments. Recombinant S. kali allergens were folded when studied by CD analysis. The sum of recombinant allergen-specific IgE levels and allergen-extract-specific IgE levels was highly correlated. Sal k 1 and profilin, reactive with IgE from 64% and 49% of patients, respectively, were the most important allergens, whereas the other S. kali allergens were less frequently recognized. Specific IgE levels were highest for profilin. Of note, 37% of patients who were negative for Sal k 1 showed IgE reactivity to Phl p 12, emphasizing the importance of the ubiquitous cytoskeletal actin-binding protein, profilin, for the diagnosis of IgE sensitization in S. kali-allergic patients. rPhl p 12 and rSal k 4 showed equivalent IgE reactivity, and the clinical importance of profilin was underlined by the fact that profilin-monosensitized patients suffered from symptoms of respiratory allergy to saltwort. Accordingly, profilin should be included in the panel of allergen molecules for diagnosis and in molecular allergy vaccines for the treatment and prevention of S. kali allergy.